An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design

• URL: http://arxiv.org/abs/2511.19726v1
• Date: Mon, 24 Nov 2025 21:41:45 GMT
• Title: An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design
• Authors: Roberto Garrone,
• Abstract summary: Multi-agent systems often operate under feedback, adaptation, and non-stationarity.<n>This paper introduces a general adaptive multi-agent learning framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent systems often operate under feedback, adaptation, and non-stationarity, yet many simulation studies retain static decision rules and fixed control parameters. This paper introduces a general adaptive multi-agent learning framework that integrates: (i) four dynamic regimes distinguishing static versus adaptive agents and fixed versus adaptive system parameters; (ii) information-theoretic diagnostics (entropy rate, statistical complexity, and predictive information) to assess predictability and structure; (iii) structural causal models for explicit intervention semantics; (iv) procedures for generating agent-level priors from aggregate or sample data; and (v) unsupervised methods for identifying emergent behavioral regimes. The framework offers a domain-neutral architecture for analyzing how learning agents and adaptive controls jointly shape system trajectories, enabling systematic comparison of stability, performance, and interpretability across non-equilibrium, oscillatory, or drifting dynamics. Mathematical definitions, computational operators, and an experimental design template are provided, yielding a structured methodology for developing explainable and contestable multi-agent decision processes.

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